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from diffusers import ( |
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StableDiffusionXLControlNetImg2ImgPipeline, |
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ControlNetModel, |
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LCMScheduler, |
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AutoencoderKL, |
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AutoencoderTiny, |
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) |
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from compel import Compel, ReturnedEmbeddingsType |
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import torch |
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from pipelines.utils.canny_gpu import SobelOperator |
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|
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try: |
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import intel_extension_for_pytorch as ipex |
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except: |
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pass |
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import psutil |
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from config import Args |
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from pydantic import BaseModel, Field |
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from PIL import Image |
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import math |
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controlnet_model = "diffusers/controlnet-canny-sdxl-1.0" |
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model_id = "stabilityai/stable-diffusion-xl-base-1.0" |
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lcm_lora_id = "latent-consistency/lcm-lora-sdxl" |
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taesd_model = "madebyollin/taesdxl" |
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default_prompt = "Portrait of The Terminator with , glare pose, detailed, intricate, full of colour, cinematic lighting, trending on artstation, 8k, hyperrealistic, focused, extreme details, unreal engine 5 cinematic, masterpiece" |
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default_negative_prompt = "blurry, low quality, render, 3D, oversaturated" |
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page_content = """ |
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<h1 class="text-3xl font-bold">Real-Time Latent Consistency Model SDXL</h1> |
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<h3 class="text-xl font-bold">SDXL + LCM + LoRA + Controlnet</h3> |
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<p class="text-sm"> |
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This demo showcases |
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<a |
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href="https://huggingface.co/blog/lcm_lora" |
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target="_blank" |
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class="text-blue-500 underline hover:no-underline">LCM LoRA</a> |
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+ SDXL + Controlnet + Image to Image pipeline using |
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<a |
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href="https://huggingface.co/docs/diffusers/main/en/using-diffusers/lcm#performing-inference-with-lcm" |
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target="_blank" |
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class="text-blue-500 underline hover:no-underline">Diffusers</a |
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> with a MJPEG stream server. |
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</p> |
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<p class="text-sm text-gray-500"> |
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Change the prompt to generate different images, accepts <a |
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href="https://github.com/damian0815/compel/blob/main/doc/syntax.md" |
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target="_blank" |
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class="text-blue-500 underline hover:no-underline">Compel</a |
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> syntax. |
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</p> |
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""" |
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class Pipeline: |
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class Info(BaseModel): |
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name: str = "controlnet+loras+sdxl" |
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title: str = "SDXL + LCM + LoRA + Controlnet" |
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description: str = "Generates an image from a text prompt" |
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input_mode: str = "image" |
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page_content: str = page_content |
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class InputParams(BaseModel): |
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prompt: str = Field( |
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default_prompt, |
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title="Prompt", |
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field="textarea", |
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id="prompt", |
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) |
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negative_prompt: str = Field( |
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default_negative_prompt, |
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title="Negative Prompt", |
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field="textarea", |
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id="negative_prompt", |
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hide=True, |
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) |
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seed: int = Field( |
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2159232, min=0, title="Seed", field="seed", hide=True, id="seed" |
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) |
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steps: int = Field( |
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2, min=1, max=15, title="Steps", field="range", hide=True, id="steps" |
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) |
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width: int = Field( |
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1024, min=2, max=15, title="Width", disabled=True, hide=True, id="width" |
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) |
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height: int = Field( |
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1024, min=2, max=15, title="Height", disabled=True, hide=True, id="height" |
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) |
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guidance_scale: float = Field( |
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1.0, |
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min=0, |
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max=20, |
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step=0.001, |
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title="Guidance Scale", |
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field="range", |
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hide=True, |
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id="guidance_scale", |
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) |
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strength: float = Field( |
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1, |
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min=0.25, |
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max=1.0, |
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step=0.0001, |
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title="Strength", |
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field="range", |
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hide=True, |
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id="strength", |
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) |
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controlnet_scale: float = Field( |
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0.5, |
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min=0, |
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max=1.0, |
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step=0.001, |
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title="Controlnet Scale", |
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field="range", |
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hide=True, |
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id="controlnet_scale", |
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) |
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controlnet_start: float = Field( |
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0.0, |
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min=0, |
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max=1.0, |
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step=0.001, |
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title="Controlnet Start", |
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field="range", |
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hide=True, |
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id="controlnet_start", |
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) |
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controlnet_end: float = Field( |
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1.0, |
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min=0, |
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max=1.0, |
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step=0.001, |
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title="Controlnet End", |
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field="range", |
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hide=True, |
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id="controlnet_end", |
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) |
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canny_low_threshold: float = Field( |
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0.31, |
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min=0, |
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max=1.0, |
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step=0.001, |
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title="Canny Low Threshold", |
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field="range", |
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hide=True, |
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id="canny_low_threshold", |
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) |
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canny_high_threshold: float = Field( |
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0.125, |
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min=0, |
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max=1.0, |
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step=0.001, |
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title="Canny High Threshold", |
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field="range", |
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hide=True, |
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id="canny_high_threshold", |
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) |
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debug_canny: bool = Field( |
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False, |
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title="Debug Canny", |
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field="checkbox", |
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hide=True, |
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id="debug_canny", |
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) |
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def __init__(self, args: Args, device: torch.device, torch_dtype: torch.dtype): |
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controlnet_canny = ControlNetModel.from_pretrained( |
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controlnet_model, torch_dtype=torch_dtype |
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).to(device) |
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vae = AutoencoderKL.from_pretrained( |
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"madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch_dtype |
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) |
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if args.safety_checker: |
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self.pipe = StableDiffusionXLControlNetImg2ImgPipeline.from_pretrained( |
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model_id, |
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controlnet=controlnet_canny, |
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vae=vae, |
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) |
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else: |
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self.pipe = StableDiffusionXLControlNetImg2ImgPipeline.from_pretrained( |
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model_id, |
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safety_checker=None, |
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controlnet=controlnet_canny, |
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vae=vae, |
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) |
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self.canny_torch = SobelOperator(device=device) |
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self.pipe.load_lora_weights(lcm_lora_id, adapter_name="lcm") |
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self.pipe.load_lora_weights( |
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"CiroN2022/toy-face", |
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weight_name="toy_face_sdxl.safetensors", |
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adapter_name="toy", |
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) |
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self.pipe.set_adapters(["lcm", "toy"], adapter_weights=[1.0, 0.8]) |
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self.pipe.scheduler = LCMScheduler.from_config(self.pipe.scheduler.config) |
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self.pipe.set_progress_bar_config(disable=True) |
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self.pipe.to(device=device, dtype=torch_dtype).to(device) |
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if device.type != "mps": |
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self.pipe.unet.to(memory_format=torch.channels_last) |
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if psutil.virtual_memory().total < 64 * 1024**3: |
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self.pipe.enable_attention_slicing() |
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self.pipe.compel_proc = Compel( |
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tokenizer=[self.pipe.tokenizer, self.pipe.tokenizer_2], |
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text_encoder=[self.pipe.text_encoder, self.pipe.text_encoder_2], |
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returned_embeddings_type=ReturnedEmbeddingsType.PENULTIMATE_HIDDEN_STATES_NON_NORMALIZED, |
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requires_pooled=[False, True], |
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) |
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if args.use_taesd: |
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self.pipe.vae = AutoencoderTiny.from_pretrained( |
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taesd_model, torch_dtype=torch_dtype, use_safetensors=True |
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).to(device) |
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if args.torch_compile: |
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self.pipe.unet = torch.compile( |
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self.pipe.unet, mode="reduce-overhead", fullgraph=True |
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) |
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self.pipe.vae = torch.compile( |
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self.pipe.vae, mode="reduce-overhead", fullgraph=True |
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) |
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self.pipe( |
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prompt="warmup", |
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image=[Image.new("RGB", (768, 768))], |
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control_image=[Image.new("RGB", (768, 768))], |
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) |
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def predict(self, params: "Pipeline.InputParams") -> Image.Image: |
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generator = torch.manual_seed(params.seed) |
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prompt_embeds, pooled_prompt_embeds = self.pipe.compel_proc( |
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[params.prompt, params.negative_prompt] |
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) |
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control_image = self.canny_torch( |
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params.image, params.canny_low_threshold, params.canny_high_threshold |
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) |
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steps = params.steps |
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strength = params.strength |
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if int(steps * strength) < 1: |
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steps = math.ceil(1 / max(0.10, strength)) |
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results = self.pipe( |
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image=params.image, |
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control_image=control_image, |
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prompt_embeds=prompt_embeds[0:1], |
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pooled_prompt_embeds=pooled_prompt_embeds[0:1], |
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negative_prompt_embeds=prompt_embeds[1:2], |
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negative_pooled_prompt_embeds=pooled_prompt_embeds[1:2], |
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generator=generator, |
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strength=strength, |
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num_inference_steps=steps, |
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guidance_scale=params.guidance_scale, |
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width=params.width, |
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height=params.height, |
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output_type="pil", |
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controlnet_conditioning_scale=params.controlnet_scale, |
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control_guidance_start=params.controlnet_start, |
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control_guidance_end=params.controlnet_end, |
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) |
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nsfw_content_detected = ( |
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results.nsfw_content_detected[0] |
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if "nsfw_content_detected" in results |
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else False |
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) |
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if nsfw_content_detected: |
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return None |
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result_image = results.images[0] |
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if params.debug_canny: |
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w0, h0 = (200, 200) |
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control_image = control_image.resize((w0, h0)) |
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w1, h1 = result_image.size |
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result_image.paste(control_image, (w1 - w0, h1 - h0)) |
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return result_image |
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